DaDianNao: A Neural Network Supercomputer

Designing A Connectionist Network Supercomputer

This paper describes an effort at UC Berkeley and the International Computer Science Institute to develop a supercomputer for artificial neural network applications. Our perspective has been strongly influenced by earlier experiences with the construction and use of a simpler machine. In particular, we have observed Amdahl's Law in action in our designs and those of others. These observations i...

Network-Based Multicomputers: A Practical Supercomputer Architecture

Multicomputers built around a general network are an attractive architecture for a wide class of applications. The architecture provides many benefits compared with special-purpose approaches, including heterogeneity, reuse of application and system code, and sharing of resources. The architecture also poses new challenges to both computer system implementors and users. First, traditional local...

Network Traffic Simulation and Assignment: Supercomputer Applications

Task Mapping on Supercomputer with Cellular Network

Several models are developed to map reasonably arbitrary application problems to computing platforms for achieving high performance by minimizing communication and by balancing computation. In our models, we assume that the underlying applications be appropriately decomposed to subtasks with known computational loads and the inter-subtask communicational demands, and assume that the computing s...

A generalized ABFT technique using a fault tolerant neural network

In this paper we first show that standard BP algorithm cannot yeild to a uniform information distribution over the neural network architecture. A measure of sensitivity is defined to evaluate fault tolerance of neural network and then we show that the sensitivity of a link is closely related to the amount of information passes through it. Based on this assumption, we prove that the distribu...